SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY, TUMAKURU VII ... 7th... · • Divulge the image formation,...

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SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY, TUMAKURU

(A Constituent College of Sri Siddhartha Academy of Higher Education)

Department of Medical Electronics

VII SEMESTER SCHEME

(2016-17 Scheme)

Sl Sub.Code Name of Subject LH T PR S C

1 ML7T01 Biomedical Digital Signal

Processing 4 0 0 0 4

2 ML7T02 Principles of Medical Imaging 4 0 0 0 4

3 ML7T03 IoT and smart sensors 3 0 0 1 4

4 ML7PE42X Elective II 3 0 0 0 3

5 ML7PE53X Elective III 3 0 0 0 3

6 ML7L01 BMDSP Lab 0 0 3 0 1.5

7 ML7L02 C++ and Python Lab 0 0 3 0 1.5

8 ML7PW01 Project Work 0 8 0 4

Total 18 00 14 01 25

LH=Lecture Hour T = Tutorial Hour

XX = CV/ ME/ EE/ EC/ CS/ PR= Practical Hour

OE= Open Elective S=Self-study Hour

Q = 1/2/3/ C= Credit

R = 1/2/3/

L = Laboratory PW = Project Work

Elective –II Credits 3-0-0-3

Sub.Code Name of the Subject Sub.Code Name of the Subject

ML7PE421 Artificial Organs and

Biomaterials ML7PE423

Linear Algebra and its

applications in medicine

ML7PE422 Adaptive Signal Processing ML7PE424 Brain Computer Interface

Elective –III Credits 3-0-0-3

Sub.Code Name of the Subject Sub.Code Name of the Subject

ML7PE531 Pattern Recognition in Medicine ML7PE533 Ergonomics and

Rehabilitation Engineering

ML7PE532 Biometrics ML7PE534 Artificial Intelligence

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SRI SIDDHARTHA INSTITUTE OF TECHNOLOGY, TUMAKURU

(A Constituent College of Sri Siddhartha Academy of Higher Education)

Department of Medical Electronics

VIII SEMESTER SCHEME

(2016-17 Scheme)

Sl.

No.

Sub.Code Name of Subject LH T PR S C

1 ML8T01 Neural Networks 4 0 0 0 4

2 ML8T02 Biomedical Therapeutic Equipments 4 0 0 0 4

3 ML8PE3X Elective IV 3 0 0 0 3

4 ML8PE4X Elective V 3 0 0 0 3

5 ML8PW02 Project Work 2 4 12 0 10

6 ML8TS01 Technical Seminar 0 0 0 1 1

Total 16 04 12 01 25

LH=Lecture Hour T = Tutorial Hour

XX = CV/ ME/ EE/ EC/ CS/ PR= Practical Hour

OE= Open Elective S=Self-study Hour

Q = 1/2/3/ numerals C= Credit

R =1/2/3

L = Laboratory PW = Project Work

TS=Technical Seminar

Elective –IV Credits 3-0-0-3

Sub.Code Name of the Subject

ML8PE311 Speech Signal Processing

ML8PE312 Smart Wearable Systems

ML8PE313 Machine Learning

ML8PE314 Clinical Data Analytics

Elective –V Credits 3-0-0-3

Sub.Code Name of the Subject

ML8PE411 ARM Processors

ML8PE412 Robotics and Automation

ML8PE413 Medical Device Development

ML8PE414 Virtual BMI

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BIOMEDICAL DIGITAL SIGNAL PROCESSING

Subject Code: ML7T01 Credits: 4-0-0-4

Duration: 4 Hr/Week No. of Hrs: 52Hrs.

Course Objectives:

• This course helps to understand the nature and difficulties to acquire bio-signal and its

processing concepts for analysis.

• It also helps to bring out the concepts related Neurological signal processing and Sleep

disorder.

• Explains the concept of data compression techniques.

• Emphasizes on Signal averaging, adaptive filers and its applications.

UNIT I: [11 Hrs]

Introduction to Biomedical Signals: The nature of biomedical signals, the action potential,

objectives of biomedical signal analysis, Difficulties in biomedical signal analysis, computer

aided diagnosis.

Neurological signal processing: The brain and its potentials, The electrophysiological origin of

brain waves, The EEG signal and its characteristics, EEG analysis.

UNIT II: [11 Hrs]

ECG Signal Processing: ECG data acquisition, ECG lead system, ECG parameters and their

estimation, ECG QRS detection techniques: Template matching, differentiation based QRS

detection techniques. Estimation of R-R Interval: Finite first difference method. The use of

multi-scale analysis for parameter estimation of ECG waveforms, Arrhythmia analysis

monitoring, long term continuous ECG recording.

UNIT III: [08 Hrs]

Sleep EEG: Data acquisition and classification of sleep stages, The Markov model and Markov

chains, Dynamics of sleep-wake transitions, Hypnogram model parameters, event history

analysis for modeling sleep.

UNIT IV: [10 Hrs]

Ecg Data Reduction Techniques: direct data compression techniques, direct ECG data

compression techniques: Turing point algorithm, AZTEC algorithm and FAN algorithm, other

data compression techniques: data compression by DPCM, data compression method

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comparison.

UNIT V: [12 Hrs]

Signal Averaging: Basics of signal averaging, signal averaging as a digital filter, a typical

averager.

Adaptive Filters: Principle of an adaptive filter, the steepest descent algorithm, adaptive noise

canceller: (a)cancellation of 60 Hz interference in electrocardiography, (b) Canceling donor-

heart interference in heart-transplant electrocardiography, (c)Cancellation of ECG signal from

the electrical activity of the chest muscles, (d)canceling of maternal ECG in fetal ECG,

(e)Cancellation of High frequency noise in Electro-surgery.

Text Books:

1. Biomedical Digital Signal Processing, Willis J. Tompkins, PHI.

2. Biomedical Signal Processing- principles and techniques by D. C. Reddy, Tata McGraw-

Hill, 2005

3. Biomedical Signal Analysis by Rangaraj M. Rangayyan, IEEE Press, 2001.

Reference Book:

1. Biomedical Signal Processing -Akay M, , Academic: Press 1994

2. Biomedical Signal Processing -Cohen.A, -Vol. I Time & Frequency Analysis, CRC

Press, 1986.

Course Outcomes: On completion of the course the student can recall

1. Understand the origin of EEG signals and their characteristics.

2. Understand the origin of ECG signals and their characteristics.

3. Understand the processing techniques required to analyze the bio medical signals

4. Understand data reduction techniques for ECG signal.

PRINCIPLES OF MEDICAL IMAGING


Duration: 4 Hr/Week No. of Hrs: 52 Hrs

Course Objectives:

• Build the physics background of interaction of radiation with matter, enabling

participants to understand projection radiography, mammography, and fluoroscopy and

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train them to assess image distortions, image attenuation for x‐ray radiography systems.

• Expose students to the developments in X‐ray Computed Tomography leading to

modern day multi‐slice, helical CT scanners and introduce the concept of computed

tomography reconstruction.

• Divulge the image formation, image quality, and imaging hardware for ultrasound

scanning. Explain the imaging principles and derive the fundamental equation of MRI.

• Expose the participants to advanced MR techniques including fast spin echoes, MR

angiography, echo planar imaging, magnetization prepared sequences, diffusion and

perfusion theory and sequences.

UNIT I:[11 Hrs]

X-rays: Introduction to Electromagnetic Spectrum, Fundamentals of X-Rays, Generation and

Detection of X-Rays, X-ray Diagnostic Methods.

UNIT II:[10 Hrs]

X-Rays: Recent Developments, X-ray Imaging Characteristics, Biological effects of Ionizing

radiation.

UNIT III:[10 Hrs]

Ultrasound: Fundamentals of Acoustic Propagation, Generation and Detection of Ultrasound,

Ultrasonic Diagnostics Methods, New Developments, Image Characteristics, Biological effects

of Ultrasounds.

UNIT IV:[11 Hrs]

Radionuclide Imaging: Fundamentals of Radioactivity, Generation and Detection of nuclear

emission, Diagnostic methods using radiation detector probes, Radionuclide Imaging Systems,

New Radionuclide Imaging methods, Characteristics of Radionuclide Images, Internal radiation

dosimeter and biological effects.

UNIT V: [10 Hrs]

Magnetic Resonance Imaging

Fundamentals of nuclear magnetic resonance, Generation and Detection of NMR signal, Imaging

Methods, In vivo NMR Spectroscopy, Characteristics of MRI, Biological Effects of Magnetic

Fields.

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Text Books:

1. Shung K. Kirk, Tsui Benjamin, Smith.B.Michael, “Principles of Medical Imaging”..

2. Suetens Paul, “Fundamentals of Medical Imaging” Cambridge University Press, 2002.

Reference Book:

1. Khandpur R.S. “Handbook of Biomedical Instrumentation”, 2nd Ed., Tata-McGRaw Hill,

2003.

Course Outcomes: On the completion of the course the students shall be able

• To gain knowledge on X-rays and its generation.

• To understand and distinguish different diagnostic method.

• To explain concepts of CT, Projection functions of CT.

• Understand the principles of Radionuclide imaging and Magnetic resonance imaging.

IoT and SMART SENSORS


Duration: 3Hr/Week No. of Hrs: 39 Hrs

Course Objectives:

• Understand the purpose of measurement, the methods of measurements, errors associated

with measurements.

• Know the principle of transduction, classifications and the characteristics of different•

transducers and study its biomedical applications.

UNIT I: [08 Hrs]

Introduction to IoT: Definition & Characteristics of IoT, Physical Design of IoT, Logical

Design of IoT, IoT Enabling Technologies, IoT Levels.

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UNIT II: [08 Hrs]

IoT System Management: Introduction, Machine-to-Machine (M2M), Difference between IoT

and M2M, SDN and NFV for IoT, Need for IoT System Management, SNMP, Network Operator

Requirements, NETCONF, YANG, IoT Systems Management with NETCONF-YANG.

UNIT III: [07 Hrs]

Domain Specific IoTs: Applications, Home Automation, Cities, Environment, Energy, Retail,

Logistics, Agriculture, Industry, health & Lifestyle.

UNIT IV: [08 Hrs]

Smart Sensors, Signal Conditioning and Control: Introduction, Smart Sensor Model,

SLEEPMODETM Operational Amplifiers, Rail – to – Rail Operational Amplifiers, Switched

Capacitor Amplifier, 4 – to 20 mA Signal Transmitter, Analog to Digital Converter, MCU

control, Modular MCU Design, DSP control.

UNIT V: [07 Hrs]

Protocols and Standards for Smart Sensors: CAN protocol, CAN Module, Neuron Chips,

MCU Protocols, IEEE 1451 working relationship, IEEE 1451.1, IEEE 1451.2, IEEE P1451.3,

IEEE P1451.4.

Text Books:

1. Internet of Things – A hands-on approach, Arshdeep Bahga and Vijay Madisetti,

Universities Press (India) Private Ltd., 2015

2. Understanding Smart Sensors, Randy Frank, 2nd Edition, Artech House Publications,

2000.

REFERENCE BOOKS:

1. Rethinking the Internet of Things: A Scalable Approach to Connecting Everything, Francis

daCosta and Byron Henderson, Apress Open, Intel Publication. 2014

2. Learning Internet of Things, Peter Waher, PACKT Publishing, 2015

3. Smart Sensor Systems, Gerard Meijer, John – Wiley and Sons, 2008.

Course Outcomes: on the completion of this course the student will be able to

1. Explain the basic design and requirement of IoT.

2. Identify the importance of different types of protocols and models used with IoT.

3. Analyze the requirements of components of smart sensors.

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4. Determine the importance of communication protocol and standards that is used with

smart sensors and improve the functionality of conventional systems using IoT.

ARTIFICIAL ORGANS AND BIOMATERIALS

Subject Code: ML7PE421 Credits: 3-0-0-3


Course Objectives:

• To create awareness to the student with modern artificial organs devices and methods

used to partially support or completely replace pathological organ

• Understand the design and working of artificial heart, kidney, and blood.

• To know about working of heart valve. Design of artificial heart valve

• Study about biomaterial which is used for design of artificial organ. Understand the

characteristics of polymeric and metallic biomaterial.

UNIT I: [07 Hrs]

ARTIFICIAL ORGANS: INTRODUCTION: Substitutive medicine, outlook for organ

replacement, design consideration, evaluation process.

ARTIFICIAL HEART AND CIRCULATORY ASSIST DEVICES: Engineering design,

Engg design of artificial heart and circulatory assist devices.

UNIT II: [07 Hrs]

ARTIFICIAL KIDNEY: Functions of the kidneys, kidney disease, renal failure, renal

transplantation, artificial kidney, dialyzers, and membranes for haemodialysis, haemodialysis

machine, peritoneal dialysis equipment-therapy format, fluid and solute removal.

ARTIFICIAL BLOOD: Artificial oxygen carriers, fluorocarbons, hemoglo bin for oxygen

carrying plasma expanders, hemoglobin based artificial blood.

UNIT III: [08 Hrs]

ARTIFICIAL LUNGS: Gas exchange systems, Cardiopulmonary bypass (heart-lung machine)-

principle, block diagram and working, artificial lung versus natural lung.

CARDIAC VALVE PROSTHESES: Mechanical valves, tissue valves, current types of

prostheses, tissue versus mechanical, engineering concerns and hemodynamic assessment of

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prosthetic heart valves, implications for thrombus deposition, durability, current trends in valve

design.

UNIT IV: [08 Hrs]

CERAMIC BIOMATERIALS: Introduction, non absorbable/relatively bioinert bioceramics,

biodegradable/restorable ceramics, bioreactive ceramics, deterioration of ceramics, bioceramic-

manufacturing techniques

POLYMERIC BIOMATERIALS: Introduction, polymerization and basic structure, polymers

used as biomaterials, sterilization, surface modifications to for improving biocompatibility.

UNIT V: [09 Hrs]

BIOMATERIALS: Introduction to biomaterials, uses of biomaterials, biomaterials in organs &

body systems, materials for use in the body, performance of biomaterials.

METALLIC BIOMATERIALS: Introduction, Stainless steel, Cobalt-Chromium alloy,

Titanium alloys, Titanium-Nickel alloys, Dental metals, Corrosion of metallic implants,

Manufacturing of implants.

Text Books:

1. Biomedical Engineering Handbook-Volume1 (2nd Edition) by J.D.Bronzino (CRC Press /

IEEE Press, 2000).

2. Biomedical Engineering Handbook-Volume 2 (2nd Edition) by J.D.Bronzino (CRC Press

/ IEEE Press, 2000)

3. Handbook of Biomedical Instrumentation (2nd Edition) by R.S.Khandpur (Tata McGraw

Hill, 2003)

Course Outcomes: on the completion of this course the student will be able to

• Understand the need of artificial organs.

• Understand the function of various organs in your body.

• Learn about the design of the various artificial organs using biomaterial.

• Understand the various biomaterials.Learn composite, biodegradable polymeric and

tissue derived materials.

LINEAR ALGEBRA AND ITS APPLICATIONS IN MEDICINE



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Course Objectives:

Solve systems of linear equations using various methods including Gaussian and GaussJordan

elimination and inverse matrices. Perform matrix algebra, invertibility, and the transpose and

understand vector algebra in Rn . Determine relationship between coefficient matrix invertibility

and solutions to a system of linear equations and the inverse matrices. Find the dimension of

spaces such as those associated with matrices and linear transformations.

UNIT I: [09 Hrs]

Linear equations: Fields; system of linear equations, and its solution sets; elementary row

operations and echelon forms Matrix operations; invertible matrices, LU-factorization.

Vector spaces: Vector spaces; subspaces; bases and dimension; coordinates; summary of row-

equivalence; computations concerning subspaces.

UNIT II: [08 Hrs]

Linear Transformations: Linear transformations; algebra of linear transformations;

isomorphism; representation of transformations by matrices; transpose of a linear transformation.

UNIT III: [07 Hrs]

Canonical Forms: Characteristic values; invariant subspaces; direct-sum decompositions;

invariant direct sums; primary decomposition theorem; cyclic bases; Jordan canonical form.

UNIT IV: [08 Hrs]

Inner Product Spaces: Inner products; inner product spaces; orthogonal sets and projections.

UNIT V: [07 Hrs]

Gram-Schmidt process; QR-factorization; least-squares problems; unitary operators

Symmetric Matrices and Quadratic Forms: Digitalization; quadratic forms; constrained

Optimization; singular value decomposition.

Text Books:

1. Gilbert Strang, "Linear Algebra and its Applications", 4thEdition, Thomson Learning Asia,

2007.

2. David C. Lay, "Linear Algebra and its Applications", 3rd Edition, Pearson Education (Asia)

Pvt. Ltd, 2005.

3. Bernard Kolman and David R. Hill, "Introductory Linear Algebra with Applications,"

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Pearson Education (Asia) Pvt. Ltd, 7th edition, 2003.

Course Outcomes:

• Understand LU factorization and elements of vector spaces.

• Learn linear transformations and least square approximations to solve inconsistent

systems, Orthonormal vectors using Gram-Schmidtt process and QR factorization.

• Understand concepts in Eigen spaces and its applications

• Understand the concept of probability, distributions and its application in Biology and

medical Science.

ADAPTIVE SIGNAL PROCESSING



Course Objectives:

UNIT I: [08 Hrs]

ADAPTIVE SYSTEMS: Definition and characteristics, Areas of application, general properties, open

and close loop adaptation, Application closed loop adaptation, examples of adaptive systems. The

adaptive linear combiner: General description, input signal and weight vectors, desired response and

error, the performance function gradient and minimum mean square error. Example of a performance

surface, alternative expression of the gradient, De correlation of error and input components.

UNIT II: [08 Hrs]

PROPERTIES OF QUADRATIC PERFORMANCE SURFACE: Normal form of input correlation

Matrix, Eigen and eigen vectors of the input correlation matrix. An example with two weights,

geometrical significance of Eigen vectors and Eigen values.

UNIT III: [08 Hrs]

SEARCHING THE PERFORMANCE SURFACE: Methods of searching the performance surface.

Basic idea of gradient search methods, A simple gradient search algorithm and its solution.

UNIT IV: [09 Hrs]

SEARCHING THE PERFORMANCE SURFACE: Methods of searching the performance surface.

Basic idea of gradient search methods, A simple gradient search algorithm and its solution. Stability and

rate of convergence, the learning curve, Gradient search by Newton’s method in multi dimensional space,

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gradient search by the method of steepest descent, comparison of learning curves.

UNIT V: [06 Hrs]

GRADIENT ESTIMATION AND EFFECTS ON ADAPTATION: Gradient component estimation by

derivatives measurements, the performance penalty, derivative measurement and performance penalties

with multiple weights.

Text Books:

1. Adaptive signal Processing- B. Widrow & S D Streans, Pearson Education 1985.

Reference Books:

1.Adaptive filters-C F N Cowan & P M Grant, Prentice Hall, 1985.

Course Outcomes:

• Describe optimal minimum mean square estimators and in particular linear estimators.

• Hypothesize Wiener filters (FIR, non-causal, causal) and evaluate their performance.

• Apply combination of theory and software implementations to solve adaptive signal problems.

• Identify applications in which it would be possible to use the different adaptive filtering

approaches.

BRAIN COMPUTER INTERFACE



Course Objectives:

This course aims for students to

(1) obtain the background to understand brain-computer interaction and human-computer

interaction; (2) understand the literature in the field of brain sensing for human-computer

interaction research; (3) understand the various tools used in brain sensing, with a focus on

functional near-infrared spectroscopy (fNIRS) research at Drexel.

(4) Understand the steps required to use real-time brain sensing data as input to an interactive

system.

(5) understand the domains and contexts in which brain-computer interfaces may be effective;

(6) Understand the open questions and challenges in brain-computer interaction research today.

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UNIT I: [08 Hrs]

Basic Neurosciences: Basic Neuroscience: Neurons, Action Potentials or Spikes, Dendrites and

Axons, Synapses, Spike Generation, Adapting the Connections: Synaptic Plasticity – (LTP,

LTD, STDP, Short-Term Facilitation and Depression), Brain Organization, Anatomy, and

Function.

Recording and Stimulating the Brain: Recording Signals from the Brain: Invasive Techniques

&Noninvasive Techniques. Stimulating the Brain - Invasive Techniques & nonTechniques.

Simultaneous Recording and Stimulation: Multi-electrode Arrays, Neurochip.

UNIT II: [08 Hrs]

Signal Processing for BCI's: Spike Sorting, Frequency Domain Analysis: Fourier analysis,

Discrete Fourier Transform (DFT), Fast Fourier Transform (FFT), Spectral Features, Wavelet

Analysis. Time Domain Analysis: Hjorth Parameters , Fractal Dimension , Autoregressive (AR)

Modeling, Bayesian Filtering, Kalman Filtering, Particle Filtering), Spatial Filtering : (Bipolar,

Laplacian, and Common Average Referencing ,Principal Component Analysis (PCA)

,Independent Component Analysis (ICA) , Common Spatial Patterns (CSP) 73 Artifact

Reduction Techniques: Thresholding, Band-Stop and Notch Filtering, Linear Modeling,

Principal Component Analysis (PCA), Independent Component Analysis (ICA).

UNIT III: [08 Hrs]

Building a BCI: Major Types of BCIs: Brain Responses Useful for Building BCIs:Conditioned

Responses, Population Activity, Imagined Motor and Cognitive Activity, Stimulus-Evoked

Activity.

Invasive BCIs: Two Major Paradigms in Invasive Brain-Computer Interfacing: BCIs Based on

Operant Conditioning, BCIs Based on Population Decoding.

UNIT IV: [09 Hrs]

Invasive BCIs in Humans: Cursor and Robotic Control Using a Multielectrode Array Implant,

Cognitive BCIs in Humans, Long-Term Use of Invasive BCIs, Long-Term BCI Use and

Formation of a Stable Cortical Representation, Long-Term Use of a Human BCI Implant

Semi-Invasive BCIs:Electrocorticographic (ECoG) BCIs -ECoG BCIs in Animals, ECoG BCIs

in Humans, BCIs Based on Peripheral Nerve Signals Nerve-Based BCIs, Targeted Muscle

Innervations (TMR).

Non-Invasive BCIs:Oscillatory Potentials and ERD, Slow Cortical Potentials, Movement

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Related Potentials, Stimulus Evoked Potentials; BCIs Based on Cognitive Tasks, Error Potentials

in BCIs, Co-adaptive BCIs, Hierarchical BCIs.

Other Noninvasive BCIs: fMRI, MEG, and fNIR: Functional Magnetic Resonance Imaging

Based BCIs, Magneto encephalography Based BCIs, Functional Near Infrared and Optical BCIs.

BCIs that Stimulate: Sensory Restoration, Restoring Hearing: Cochlear Implants, Restoring

Sight: Cortical and Retinal Implants, Motor Restoration, Deep Brain Stimulation (DBS), Sensory

Augmentation.

UNIT V: [06 Hrs]

Medical Applications: Sensory Restoration, Motor Restoration, Cognitive Restoration,

Rehabilitation, Restoring Communication with Menus, Cursors, and Spellers, Brain Controlled

Wheelchairs

Nonmedical Applications: Web Browsing and Navigating Virtual Worlds, Robotic Avatars,

High Throughput Image Search Lie Detection and Applications in Law , Monitoring Alertness,

Estimating Cognitive Load, Education and Learning, Security, Identification, and

Authentication, Physical Amplification with Exoskeletons, Mnemonic and Cognitive

Amplification , Applications in Space, Gaming and Entertainment, Brain-Controlled Art.

Ethics of Brain-Computer Interfacing: Medical, Health, and Safety Issues, Balancing Risks

versus Benefits, Informed Consent, Abuse of BCI Technology, BCI Security and Privacy, Legal

Issues, Moral and Social-Justice Issues.

Text Books:

[1] Brain-Computer Interfacing: An Introduction (1 Edition) by Rajesh P. N. Rao

[2] Brain-Computer Interfaces: Revolutionizing Human-Computer Interaction (The Frontiers

Collection) Hardcover – (13 Dec 2010) by Bernhard Graimann (Editor), Brendan Z. Allison

(Editor), GertPfurtscheller (Editor)

Course Outcomes:

• Apply the knowledge of mathematics science and engineering fundamentals to

understand the Brain Organization.

• Apply the knowledge of mathematics science and engineering fundamentals to

understand the brain anatomy and Function.

• Analyze and process the brain signals for artifact reduction.

• Understand types of BCI, principles and its applications which are present state of art in

the Neurosciences domain.

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PATTERN RECOGNITION IN MEDICINE



Course Objectives:

Pattern recognition techniques are used to design automated systems that improve their own

performance through experience. This course covers the methodologies, technologies, and

algorithms of statistical pattern recognition from a variety of perspectives. Topics including

Bayesian Decision Theory, Estimation Theory, Linear Discrimination Functions, Nonparametric

Techniques, Decision Trees, and Clustering Algorithms etc. will be presented.

UNIT I: [08 Hrs]

Introduction: Machine perception, pattern Recognition systems, Design cycles, learning and

adaptation.

Probability: Random variable, joint distribution and densities, moments of random variable,

Estimation of parameters from sample.

UNIT II: [08 Hrs]

Statistical decision making: Introduction, Baye’s theorem, multiple features, conditionally

independent features, decision bounderies, unequal costs of error, estimation of error rates,

characteristic curves, problems. (3.1-3.7, 3.9 from text 1).

UNIT III: [08 Hrs]

Non parametric Decision making: Introduction, Histograms, kernel and window estimators,

nearest neighbor classification techniques, adaptive decision boundaries, adaptive discriminate

functions, minimum squared error discriminant functions. (4.1-4.7 text 1)

UNIT IV: [07 Hrs]

Clustering: Introduction, Hierarchical clustering, partitional clustering, Unsupervised Bayesion

learning, Hierarchical clustering, partitional clustering, problems.

UNIT V: [08 Hrs]

Processing of waveforms and images: Introduction, gray level scaling transformations,

equalization, geometric image scaling and interpolation, edge detection, laplacian and sharpening

operators, line detection and template matching, logarithmic gray level scaling. (7.1-7.9 text 1)

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Text Books:

1. Pattern Recognition and Iamge Analysis, Earl Gose, Richard Johnson Baugh and Steve

jost, PHI

Reference Book:

1. Richard O.Duda, Peter E.Herd and David & Stork, pattern classification, john Wiley

and sons, Inc 2nd Ed.2001.

2. Robert Schlkoff, Pattern Recognition: Statistical Structural and Neural Approaches,

John Wiley and sons, Inc, 1992.

Course Outcomes:

• Understand the basic concepts of Pattern Recognition and its applications

• Apply the concepts of joint distribution & densities and risk estimators of events.

• Understand Statistical decision making and Non parametric decision making

• Understand the concepts of clustering - hierarchical clustering and partitional clustering

and analysis of wave forms and Image.

BIOMETRICS



Course Objectives:

• To understand the state-of-the-art in biometric technologies;

• To survey the currently available biometric systems;

• To explore ways to improve some of the current techniques;

• To learn and implement some of the biometrics authentication;

• To explore new techniques

UNIT I: [08 Hrs]

Introduction – Benefits of biometric security – Verification and identification – Basic working of

biometric matching – Accuracy – False match rate – False non-match rate – Failure to enroll rate

– Derived metrics – Layered biometric solutions.

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UNIT II: [08 Hrs]

Finger scan – Features – Components – Operation (Steps) – Competing finger Scan technologies

– Strength and weakness. Types of algorithms used for interpretation. Voice Scan - Features –

Components – Operation (Steps) – Competing voice Scan (facial) technologies – Strength and

weakness.

UNIT III: [08 Hrs]

Iris Scan - Features – Components – Operation (Steps) – Competing iris Scan technologies –

Strength and weakness. Facial Scan - Features – Components – Operation (Steps) – Competing

facial Scan technologies – Strength and weakness.

UNIT IV: [07 Hrs]

Other physiological biometrics – Hand scan – Retina scan – AFIS (Automatic Finger Print

Identification Systems) – Behavioral Biometrics – Signature scan- keystroke scan.

UNIT V: [08 Hrs]

Biometrics Application – Biometric Solution Matrix – Bio privacy – Comparison of privacy

factor in different biometrics technologies – Designing privacy sympathetic biometric systems.

Biometric standards – (BioAPI , BAPI) – Biometric middleware. Biometrics for Network

Security: Statistical measures of Biometrics. Biometric Transactions.

Text Books:

1. Biometrics–Identity Verification in a Networked World–Samir Nanavati, Michael Thieme,

Raj Nanavati, Wiley India Pvt Ltd, 2002 .

2. Biometrics for Network Security- Paul Reid, Pearson Education, 2004.

Reference Book:

1. Biometrics- The Ultimate Reference- John D. Woodward, Jr. Wiley Dreamtech.

2. Biometric Systems Technology, Design and Performance Evaluation, James Wayman, Anil

Jain, Davide Maltoni and Dario Maio, Springer Publications.

3. Personal Identification in Networked Society, Jain, A.K.; R Bolle, Ruud M.; S Pankanti,

Sharath, 1st ed. 1999. 2nd printing, 2006, Springer Publications.

4. Handbook of Biometrics, Jain, Anil K.; Flynn, Patrick; Ross, Arun A, Springer, 2008.

Course Outcomes:

• Understand the fundamentals and the need of biometrics.

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• Learn the deployment, strength & weakness of the types of Biometrics.

• Learn the uncommon biometrics and its usage.

• Understand the applications of Biometrics and learn the risks, standards and testing /

Evaluation process of Biometrics.

ERGONIMICS AND REHABILITATION ENGINEERING



Course Objectives:

This course covers the use of ergonomic principles to recognize, evaluate, and control workplace

conditions that cause or contribute to musculoskeletal and nerve disorders. Course topics include

work physiology, anthropometry, musculoskeletal disorders, use of video display terminals, and

risk factors such as vibration, temperature, material handling, repetition, and lifting and patient

transfers in health care. Course emphasis is on industrial case studies covering analysis and

design of work stations and equipment workshops in manual lifting, and coverage of current

OSHA compliance policies and guidelines.

UNIT I: [08 Hrs]

Introduction : Focus of ergonomics & its applications, Body mechanics: Basics, Anatomy of

Spine & pelvis related to posture, postural stability & adaptation, Low back pain, risk factors

formusculo skeletal disorders in workplaces, Anthropometric principles in workspace: Designing

for a population of users, Human variability sources, applied anthropometry in ergonomics &

design, anthropometry & personal space.

UNIT II: [07 Hrs]

Design of Repetitive Tasks: Work related musculoskeletal disorders, injuries to upper body at

work, neck disorders, carpal tunnel syndrome, tennis elbow, shoulder disorder, ergonomic

interventions. Design of physical environment: human thermoregulation, thermal environment,

working in hot & cold climates, skin temperature, protection against extreme climates, comfort

& indoor climate, ISO standards.

UNIT III: [08 Hrs]

Engineering Concepts in Rehabilitation Engineering: Anthropometry: Methods for Static and

dynamic Measurements: Area Measurements, Measurement of characteristics and movement,

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Ergonomic aspects in designating devices: Introduction to Models in Process Control, Design of

Information Devices, Design of Controls Active Prostheses: Active above knee prostheses.

Myoelectric hand and arm prostheses- different types, block diagram, signal flow diagram and

functions. The MARCUS intelligent Hand prostheses.

UNIT IV: [08 Hrs]

engineering concepts in sensory rehabilitation engineering: Sensory augmentation and

substitution: Visual system: Visual augmentation, Tactual vision substitution, and Auditory

vision substitution. Auditory system: Auditory augmentation, Audiometer, Hearing aids,

cochlear implantation, visual auditory substitution, tactual auditory substitution, Tactual system:

Tactual augmentation, Tactual substitution.

UNIT V: [08 Hrs]

Orthopedic Prosthetics and Orthotics in rehabilitation: Engineering concepts in motor

rehabilitation, applications. Computer Aided Engineering in Customized Component Design.

Intelligent prosthetic knee, A hierarchically controlled prosthetic and A self-aligning orthotic

knee joint. Externally powered and controlled Orthotics and Prosthetics. FES systems-

Restoration of hand function, restoration of standing and walking, Hybrid Assistive Systems

(HAS).

Text Books:

1. Introduction to Ergonomics by R S Bridger, Rout ledge Taylor & Francis group,

London,2008 2. Bronzino, Joseph; Handbook of biomedical engineering.

2. 2nd edition, CRC Press, 2000. 24 3. Robinson C.J Rehabilitation engineering. CRC press

1995.

Reference Book:

1. Fitting the task to human, A textbook of occupational ergonomics, 5th edition, Taylor

&Francis, ACGIH publications , 2008

2. Work study & Ergonomics by DhanpatRai& sons, 1992

3.Horia- NocholaiTeodorecu, L.C.Jain , Intelligent systems and technologies in

rehabilitation engineering; CRC; December 2000.

4. Etienne Grandjean, Harold Oldroyd, Fitting the task to the man, Taylor & Francis,1988.

Course Outcomes: On completion of this course, the students shall be able to

20

1. Understand the principles behind the ergonomics and rehabilitation engineering and

analyze the task oriented principles of ergonomics.

2. Understand the visual, augmented principles of rehabilitation engineering.

3. To demonstrate the sensory principles for various applications.

4. Demonstrate an understanding of the basic concepts of assistive devices as prosthetic

implants in ortho related applications.

ARTIFICIAL INTELLIGENCE



Course Objectives:

• To create appreciation and understanding of both the achievements of AIand the theory

underlying those achievements.

• To impart basic proficiency in representing real life problems in a state space representation so

as to solve them using different AI techniques.

• To create an understanding of the basic issues of knowledge representation and heuristic search

techniques.

UNIT I: [08 Hrs]

Introduction: What is Artificial Intelligence?, AI Problems, The underlying Assumption, What

is an AI Technique, Problems, problem spaces, and search Defining the problem as a State Space

Search, Production Systems, Problem Characteristics, Production System Characteristics, Issues

in the Design of search programs, Additional Problems.

UNIT II: [08 Hrs]

Heuristic and Search Techniques: Generate-and-Test, Hill Climbing, Best-First Search,

Problem Reduction, Constraint satisfaction, Means-Ends Analysis

UNIT III: [08 Hrs]

Knowledge Representation Issues: Representation and Mappings, Approaches to knowledge

Representation, Issues in knowledge Representation, Weak Slot Filler Structures: Semantic Nets,

Frames

UNIT IV: [08 Hrs]

Using Predicate Logic: Representing the simple facts in logic, Representing Instance and ISA

21

Relationships, Computable functions and predicates, Resolution, Natural Deduction

UNIT V: [07 Hrs]

Strong slot-and-Filter Structures : Conceptual Dependency, Scripts, CYC Expert Systems

Representation and Using Domain Knowledge, Expert Systems shells, Explanation, Knowledge

Acquisition.

Text Books:

1. Elaine Rich, Kevin Knight, Shivashankar B Nair: Artificial Intelligence, 3rd Edition, Tata

McGraw Hill, 1991.

Reference Books:

1. Stuart Russel, Peter Norvig: Artificial Intelligence A Modern Approach, 2nd Edition, Pearson

Education, 2003.

2. Nils J. Nilsson: Principles of Artificial Intelligence, Elsevier, 1980.

Course Outcomes:

On completion of this course, the students shall be able to

1. Demonstrate the knowledge of building blocks of AI.

2. Analyze and formalize the problem as a state space tree, design heuristics and solve using

different search techniques.

3. Analyze and demonstrate knowledge representation using various techniques.

4. Develop AI solutions for a given problem.

BIOMEDICAL DIGITAL SIGNAL PROCESSING LAB

Subject Code: ML7L01 Credits: 0-0-3-1.5

Duration: 3 Hr/Week

Course Objectives:

1. To understand the basic signals in the field of biomedical.

2. To study origins and characteristics of some of the most commonly used biomedical

signals, including ECG, EEG, evoked potentials, and EMG.

3. To understand Sources and characteristics of noise and artifacts in bio signals.

4. To understand use of bio signals in diagnosis, patient monitoring and physiological

investigation.

22

1. Computation of Convolution and Correlation Sequences.

2. Signal Averaging to Improve the SNR

3. Read and plotting of ECG data, spectrum of ECG with 50 HZ noise.

4. Design of FIR Filter for ECG.

5. Integer filters for ECG

6. QRS detection and Heart rate determination.

7. Correlation and Template matching.

8. Realization of Notch filter for removal of line interference

9. Data Compression Techniques using AZTEC algorithm.

10. Data Compression Techniques using TP algorithm.

11. Data Compression Techniques using FAN algorithm.

Note: The above experiments are to be conducted using Matlab/ Lab VIEW/ “C” language.

Text Books:

1. Bioelectrical Signal Processing in Cardiac & Neurological Applications - Leif SSrnmo ,

Pablo Laguna - Elsevier - Academic Press.

Reference Book:

1. Biomedical Digital Signal Processing, Willis J. Tompkins, PHI.

2. Biomedical Signal Processing- principles and techniques by D. C. Reddy, Tata McGraw-

Hill, 2005

3. Biomedical Signal Analysis by Rangaraj M. Rangayyan, IEEE Press, 2001.

Course Outcomes:

• Understand the nature of biomedical signals, objectives of signal analysis, difficulties in

biomedical signal analysis

• Different types of noise that can corrupt biomedical signals, filters used to remove

artifacts.

• Understand the processing concepts for analysis, acquisition and classification of sleep

using EEG signal.

• Understand and apply various data compression techniques on different types of

biomedical signals.

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C++ AND PYTHON LAB

Subject Code: ML7L02 Credits: 0-0-3-1.5

Duration: 3 Hr/Week

C++ Lab:

1. Write a C++ program to calculate the sum of the series i) 1+x+x2+x3+...+xn

ii) -1+2-4+8-16+...1024

2. Write a C++ program to sort the elements of an array using i) Selection sort ii) Bubble sort

3. Write a C++ program to accept two arrays of different lengths. Merge the two accepted arrays.

4. Write a C++ program to accept two 2-dimensional arrays and perform addition, subtraction

and multiplication.

5. Write a C++ program to find the LCM and GCD of 2 given numbers using functions.

6. Write a C++ program to find the factorial of a given number using recursive function.

7. Write a C++ program to find the largest, smallest and their averages using functions.

8. Write a C++ program to accept the information about an employee and calculate the following

and display using structure.

i) Accept the basic salary, name, id_no of an employee.

ii) Calculate DA, HRA, PF, LIC, Gross and net salary.

DA: 45% of basic salary

HRA: If basic is >=2000 and <3000, HRA=800

If basic is >=3000 and <4000, HRA=1000

If basic is >=4000 and <6000, HRA=1200

If basic is >=6000, HRA=1500

PF: 11.5% of basic salary

LIC: 17% of basic salary

Gross=basic salary+DA+HRA

Net salary=Gross-PF-LIC

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9. Write a C++ program to find the sum of two complex numbers using classes by overloading

operator +.

10. Write a C++ program to multiply two numbers using Multiple Inheritance.

Python Lab:

2. Basic programs using python:

i) Display of a word/ sentence.

ii) Performing calculations.

iii) Use of variables and objects.

iv) Use of loops, arrays, functions, plots.

Text Books:

1. Object Oriented programming in TURBO C++ ,Robert Lafore, Galgotia

Publications.2002.

2. Classic Data Structures, Debasis Samanta, Second Edition, PHI, 2009.

Reference Book:

1. Object Oriented Programming with C++ ,E.Balaguruswamy, third edition, TMH 2006

2. C++ the complete reference, Herbert Schildt, fourth edition, TMH, 2003.

Course Outcomes: By the completion of this course, the student will be able to:

• know how to use data types based on the programs and declare variables.

• Learn the concepts and importance of functions, arrays, classes & objects.

• Understand the concept of Operator Overloading and inheritance for effective

programming.

• learn the basic concepts of python.

25

NEURAL NETWORKS


Duration: 4Hr/Week No. of Hrs: 52 Hrs

Course Objectives:

This course gives an introduction to basic neural network architectures and learning rules.

Emphasis is placed on the mathematical analysis of these networks, on methods of training them

and on their application to practical engineering problems in such areas as pattern recognition,

signals processing and control systems.

UNIT I: [10 Hrs]

Introduction: The classic neuron, Membrane potential, Action potential, Neuronal electrical

behavior, Cable Equation, Synaptic Integration. Models of Neuron, Synaptic Electrical Events,

slow potential theory of neuron, two state neurons, Feedback.

UNIT II: [11 Hrs]

Network Architectures: Single layer feed forward networks; Multilayer feed forward networks,

Recurrent Networks, Knowledge representation.

UNIT III: [10 Hrs]

Learning processes: Introduction Error correction learning, Memory based learning, Hebbian

Learning, Competitive learning.

UNIT IV: [10 Hrs]

Learning paradigms: Learning with a teacher, Learning without a teacher, Learning tasks,

Memory, Adaptation Artificial intelligence and Neural networks.

UNIT V: [11 Hrs]

Information representation in biological Systems, Distributed, Map, layered structures, Visual

system, Auditory System.

Text Books:

1. James A. Anderson—An Introduction to neural networks, 2e, PHI, 1995

2. Simon Haykin—Neural Networks, Pearson education PHI 2001.

Reference Book:

26

1. Mohammad Hasan- Fundamentals of Artificial Neural Networks, PHI, 1999

Course Outcomes: on the completion of this course the students will be able to

• The fundamental concepts of artificial neural network.

• Network architectures and its principles.

• Different learning algorithms and its applications.

• Information representation in biological system and its models.

BIOMEDICAL THERAPEUTIC EQUIPMENTS



Course Objectives:

The objective of this course is to introduce the students to the application of biomedical

instrumentation used in surgery. This course is to familiarize the students with physiotherapy and

electrotherapy instruments and various machines used in ICU . It includes brief study of different

types of ventilators and how to design a automated drug delivery unit depends on the

requirement of patient.

UNIT I: [10 Hrs]

Instruments for Surgery: Principles of surgical diathermy, surgical diathermy Machine, safety

aspects in electro- surgical units, surgical diathermy Analyzer.

UNIT II: [10 Hrs]

Physiotherapy and Electrotherapy Equipments: High frequency heat therapy, Shortwave

diathermy, microwave diathermy, ultrasound therapy unit, Electro diagnostic therapeutic

apparatus, pain relief through electrical Stimulation, bladder and cerebella stimulators.

UNIT III: [10 Hrs]

Haemodialysis Machine: Artificial kidney, dialyzer, Membranes for haemodialysis.

Lithotripters: Stone disease problems, lithotripter machine, extra-corporeal Shock wave therapy.

Anesthesia Machine: Need for anesthesia, anesthesia Machine

UNIT IV: [10 Hrs]

Ventilators: Artificial ventilation, ventilators, types of ventilators, ventilators terms,

classification of ventilators. Modern ventilators. Humidifiers, Nebulizers and Aspirators

27

UNIT V: [12 Hrs]

Automated Drug Delivery Systems: Infusion pumps, components of drugs infusion systems

and implantable infusion systems. Closed Loop Control Infusion Pumps.

Text Books:

1. Handbook of Biomedical Instrumentation – by R.S.Khandpur, 2 McGraw Hill, 2003.

2. Biomedical Instrumentation by Dr.M. Arumugam-Second Edition- 1994.

Course Outcomes:

• Learn the working principle of Instruments for surgery and physiotherapy, electrotherapy

instruments

• Understand the working of kidney, design of artificial kidney. Advantages and need of

anesthesia machine.

• Understand the principles of ventilators, study about different types of ventilators.

• Analyzing the concepts of Automated Drug delivery Systems.

MACHINE LEARNING



Course Objectives:

The main goal of this course is to help students learn, understand, and practice big data analytics

and machine learning approaches, which include the study of modern computing big data

technologies and scaling up machine learning techniques focusing on industry applications.

Mainly the course objectives are: conceptualization and summarization of big data and machine

learning, trivial data versus big data, big data computing technologies, machine learning

techniques, and scaling up machine learning approaches.

UNIT I: [08 Hrs]

Introduction: Introduction to machine learning, Examples of Machine Learning Applications.

Parametric regression: linear regression, polynomial regression, locally weighted regression,

numerical optimization, gradient descent, kernel methods.

28

UNIT II: [08 Hrs]

Generative learning: Gaussian parameter estimation, maximum likelihood estimation, MAP

estimation, Bayesian estimation, bias and variance of estimators, missing and noisy features,

nonparametric density estimation, Gaussian discriminant analysis, naive Bayes. Discriminative

learning: linear discrimination, logistic regression, logit and logistic functions, generalized linear

models, softmax regression.

UNIT III: [08 Hrs]

Neural networks: the perceptron algorithm, multilayer perceptrons, backpropagation, nonlinear

regression, multiclass discrimination, training procedures, localized network structure,

dimensionality reduction interpretation.

Support vector machines: functional and geometric margins, optimum margin classifier,

constrained optimization, Lagrange multipliers, primal/dual problems, KKT conditions, dual of

the optimum margin classifier, soft margins, kernels, quadratic programming, SMO algorithm.

UNIT IV: [07 Hrs]

Graphical and sequential models: Bayesian networks, conditional independence, Markov random

fields, inference in graphical models, belief propagation, Markov models, hidden Markov

models, decoding states from observations, learning HMM parameters.

UNIT V: [08 Hrs]

Unsupervised learning: K-means clustering, expectation maximization, Gaussian mixture density

estimation, mixture of naive Bayes, model selection. Dimensionality reduction: feature selection,

principal component analysis, linear discriminant analysis, factor analysis, independent

component analysis, multidimensional scaling, and manifold learning.

Text Books:

1). Elements of Statistical Learning, T. Hastie, R. Tibshirani and J. Friedman, Springer, 2001. 2).

Machine Learning, EthemAlpaydin, MIT Press, 2010.

.

Reference Books :

1). Pattern Recognition and Machine Learning, C. Bishop, Springer, 2006.

2). Machine Learning: A Probabilistic Perspective, K. Murphy, MIT Press, 2012.

3). Pattern Classification, R. Duda, E. Hart, and D. Stork, Wiley-Interscience, 2000.

29

4). Machine Learning, T. Mitchell, McGraw-Hill, 1997.

Course Outcomes:

• Apply the knowledge of mathematics science and engineering fundamentals in the

understanding of fundamental issues and challenges of machine learning: data, model

selection, model complexity, etc.

• Analyze the strengths and weaknesses of many popular machine learning approaches.

• Comprehend the underlying mathematical relationships within and across Machine

Learning algorithms and the paradigms of supervised and un-supervised learning.

• Design and implement various machine learning algorithms in a range of real-world

applications.

SMART WEARABLE SYSTEMS



Course Objectives:

Extensive efforts have been made in both academia and industry in the research and development

of smart wearable systems (SWS) for health monitoring (HM). Primarily influenced by

skyrocketing healthcare costs and supported by recent technological advances in micro- and

nanotechnologies, miniaturisation of sensors, and smart fabrics, the continuous advances in SWS

will progressively change the landscape of healthcare by allowing individual management and

continuous monitoring of a patient’s health status. Consisting of various components and

devices, ranging from sensors and actuators to multimedia devices, these systems support

complex healthcare applications and enable low-cost wearable, non-invasive alternatives for

continuous 24-h monitoring of health, activity, mobility, and mental status, both indoors and

outdoors. Our objective has been to examine the current research in wearable to serve as

references for researchers and provide perspectives for future research

UNIT I: [08 Hrs]

Introduction : What is Wearable Systems, Need for Wearable Systems, Drawbacks of

Conventional Systems for Wearable Monitoring, Applications of Wearable Systems, Recent

developments – Global and Indian Scenario, Types of Wearable Systems, Components of

wearable Systems, Physiological Parameters commonly monitored in wearable applications,

Smart textiles, & textiles sensors, Wearable Systems for Disaster management, Home Health

care, Astronauts, Soldiers in battle field, athletes, SIDS, Sleep Apnea Monitoring.

30

UNIT II: [08 Hrs]

Smart Sensors& Vital Parameters : Vital parameters monitored and their significances, Bio-

potential signal recordings (ECG, EEG, EMG), Dry Electrodes design and fabrication methods,

Smart Sensors – textile electrodes, polymer electrodes, non-contact electrodes, MEMS and Nano

Electrode Arrays, Cuff-less Blood Pressure Measurement, PPG, Galvanic Skin Response (GSR),

Body Temperature Measurements, Activity Monitoring for Energy Expenditure, Respiratory

parameters.

UNIT III: [08 Hrs]

Wearable Computers : Flexible Electronics, Wearable Computers, Signal Processors, Signal

Conditioning circuits design, Power Requirements, Wearable Systems Packaging, Batteries and

charging, Wireless Communication Technologies and Protocols, Receiver Systems, Mobile

Applications based devices.

UNIT IV: [07 Hrs]

Wireless Body Area Networks: Wireless Body Area Networks – Introduction, Personal Area

Networks (PAN), Application in Vital Physiological Parameter monitoring, Design of Sensor &

Sink Nodes, Architecture, Communication & Routing Protocols, Security, Power and Energy

Harvesting.

UNIT V: [08 Hrs]

Data Processing And Validation : Classification Algorithms, Data Mining and Data Fusion,

Signal Processing Algorithms in wearable Applications, Issues of wearable physiological

monitoring systems, Statistical Validation of Parameters, Certifications of Medical Devices and

Patenting.

Text Books:

1. Annalisa Bonfiglo, Danilo De Rossi, Wearable Monitoring Systems, Springer, 2011

2. Edward Sazonov, Micheal R Neuman, Wearable Sensors: Fundamentals, Implementation and

Applications, Elseiver, 2014.

Reference Books:

1. Kate Hartman, Make: Wearable Electronics: Design, Prototype and wear your own interactive

garments, Maker Media

2. Elijah Hunter, Wearable Technology, Kindle Edition

3. Guang Zhong Yang, Body Sensor Networks, Springer .

Course Outcomes: On completion of this course, the students shall be able to

1. Understand the basic foundations on biological and artificial neural network and the

importance of neuron models for pattern classification

2. Demonstrate the process of forming association between related patterns through associative

networks

31

3. Apply the principles of back propagation supervised learning for error minimization

4. Understand and analyze the various competition based learning algorithms and importance of

resonance based network learning algorithms.

SPEECH SIGNAL PROCESSING



Course Objectives:

i) To understand the characteristics of speech signal,

ii) To apply signal processing concepts to speech signal,

iii) To get an insight into a few applications of speech processing.

UNIT I: [08 Hrs]

Digital Models For Speech Signals: Process of Speech Production, Lossless tube models,

Digital models for Speech signals.

Time Domain Models For Speech Processing: Time dependent processing of speech, Short

time energy and average magnitude, Short time average zero crossing rate, Speech Vs silence

discrimination using energy and zero crossing.

UNIT II: [08 Hrs]

Short Time Fourier Analysis: Linear filtering interpretation, Filter bank summation method,

Design of digital filter banks, Implementation using FFT, Spectrographic displays.

UNIT III: [08 Hrs]

Digital Representations Of The Speech Waveform: Sampling speech signals, Review of the

statistical model for speech, Instantaneous quantization, Adaptive Quantization, General theory

of differential quantization, Delta modulation.

UNIT IV: [07 Hrs]

Linear Predictive Coding Of Speech: Basic principles of linear predictive analysis, Solution of

LPC equations, Prediction error signal, Frequency domain interpretation, Relation between the

various speech parameters, Applications of LPC parameters.

32

UNIT V: [08 Hrs]

Speech Synthesis: Principles of Speech synthesis, Synthesis based on waveform coding,

analysis synthesis method, speech production mechanism, Synthesis by rule, Text to speech

conversion.

Speech Recognition: Principles of Speech recognition, Speech period detection, Spectral

distance measures, Structure of word recognition systems, Dynamic time warping (DTW), Word

recognition using phoneme units.

Text Books:

1. Digital Processing of Speech Signals- L R Rabiner and R W Schafer, Pearson

Education 2004.

2. Digital Speech Processing- Synthesis and Recognition, Sadoaki Furui, 2nd

Edition, Mercel Dekker 2002.

.

Reference Books:

1. Introduction to Data Compression- Khalid Sayood, 3rd Edition, Elsivier

Publications.

2. Digital Speech-A M Kondoz, 2nd Edition, Wiley Publications

Course Outcomes: On completion of the course the student can recall

• Properties of speech signal and its production and discrimination system

• Design of filter bank and its implementation, and spectrographic display.

• Digital representation of speech signal using different quantization techniques.

• LPC algorithms and its applications for speech coding and fundamental algorithms for

speech synthesis, coding and recognition.

33

CLINICAL DATA ANALYTICS



Course Objectives:

• Identify key tools and approaches to improve analytics capabilities in clinical settings.

• Describe different governance and operations strategies in analytics in clinical settings.

• Discuss value-based payment systems and the role of data analytics in achieving their potential.

• Analyze data used in population management and value-based care systems.

UNIT I: [08 hours]

Introduction to Biostatistics: Introduction, Some basic concepts, Measurement and

Measurement Scales, Simple random sample, Computers and biostatistician analysis. Descriptive

Statistics: Introduction, ordered array, grouped data-frequency distribution, descriptive statistics

– measure of central tendency, measure of dispersion, measure of central tendency probability

distributions of discrete variables, binomial distribution, Poisson distribution, continuous

probability distribution, normal distribution.

UNIT II: [08 hours]

Sampling distributions: distribution of sample mean, distribution of the difference between two

sample means, distribution of sample proportion, distribution of the difference between two

sample proportions, Estimation: confidence interval for a population mean, t-distribution,

confidence interval for differences between two population means, confidence interval for a

population proportion, confidence interval for difference between two populations determination

of sample size for estimating means, for estimating proportions , confidence interval for the

variance of normally distributed population, confidence interval for ratio of variances of two

normally distributed populations.

UNIT III: [07 hours]

Hypothesis Testing : Introduction, hypothesis testing – single population mean, difference

between two population means, paired comparisons, hypothesis testing-single population

proportion, difference between two population proportions, single population variance, ratio of

two population variances.

34

UNIT IV: [08 Hrs]

Analysis of Variance (ANOVA): Introduction, completely randomized design, randomized

complete block design, repeated measures design, factorial experiment UNIT-5 8 hours Linear

Regression and Correlation: the regression model, sample regression equation, evaluating and

using regression equation, correlation model correlation coefficient Multiple linear regression

model, obtaining multiple regression equation, evaluating multiple regression equation, using the

multiple regression equation, multiple correlation model, mathematical properties of Chisquare

distribution.

UNIT V: [08 Hrs]

Linear Regression and Correlation: the regression model, sample regression equation, evaluating

and using regression equation, correlation model correlation coefficient Multiple linear

regression model, obtaining multiple regression equation, evaluating multiple regression

equation, using the multiple regression equation, multiple correlation model, mathematical

properties of Chi-square distribution.

Text Books:

1. 1. “Biostatistics-A Foundation for Analysis in the Health Sciences” Wayne W. Daniel, John

Wiley & Sons Publication, 6th Edition

2. Fundamentals of Biiostatistics by khan and khanum, Ukaaz publications, 2nd revise edition

3. “An introduction to statistical Method and data analysis”, by R. Lyman ott..

Course Outcomes:

• Ability to apply knowledge of mathematics, science and Engineering to develop the

solution using biostatistical concepts.

• Ability to analyse a problem and formulate appropriate solution for biostatistical concepts

application.

• An ability to design and perform statistical test and interpret results

• Ability to implement and demonstrate statistical analysis using modern tool usage.

ARM PROCESSORS



Course Objectives:

• This course introduces the concept of architecture and programming of advanced

embedded microcontrollers i.eARMfamily of microcontrollers that are widely used in

design of real time sophisticated embedded systems like tablets, hand held devices,

35

automation and industrial control systems.

• It also covers writing Embedded C programming of LPC2148 for GPIO,ADC,DAC,

UART, LCD, Timers and etc.

• It also explains the concepts of embedded system and its components.

UNIT I: [08 Hrs]

ARM EMBEDDED SYSTEMS

The RISC Design Philosophy, The ARM Design Philosophy, Embedded System Hardware,

Embedded System Software.

ARM PROCESSOR FUNDAMENTALS

Registers, Current Program Status Register, Pipeline, Exceptions, Interrupts, and Vector Table,

Core Extensions, Architecture Revisions, ARM Processor Families, LPC2148 Microcontroller

Architecture, Memory Mapping, Register Description.

UNIT II: [07 Hrs]

INTRODUCTION TO THE ARM INSTRUCTIONS SET

Data Processing Instructions, Branch Instructions, Load-Store Instructions, Software Interrupt

Instructions, Program Status Register Instruction, Example Programs.

UNIT III: [08 Hrs]

INTRODUCTION TO THE ARM INSTRUCTIONS SET contd….

Loading Constants, ARMv5E Extensions, Conditional Execution, and Example Programs.

EFFICIENT C PROGRAMMING

Overview of C Compilers and Optimization, Basic C Data Types, C Looping Structures, Register

Allocation, Function Calls, Pointer Aliasing, Structure Arrangement, Bit-fields, Unaligned Data

and Endianness, Division, Floating Point, Inline Functions and Inline Assembly.

UNIT IV: [08 Hrs]

Interfacing

Sensors, Actuators, GPIO, LED, 7 segment display, stepper motor, Keyboard, Push button

switch, Data Conversions (ADC, DAC), Timers, Communication Protocols: UART, I2C, SPI,

CAN(onboard), Programs using C.

UNIT V: [08 Hrs]

Embedded System Components

Embedded v/s General computing system, Classification of Embedded systems, Major

applications and purpose of Embedded systems. Core of an Embedded System including all

types of processor/controller, Memory.

Text Books:

1.ARM Systems Developer's Guide Designing and Optimizing System Software, Andrew N.

Sloss, Dominic Symes, Chris Wright, Morgan Kaufmann Publishers, ElseveirInc,

2004.(Chapters 1, 2, 3, 5)

2. Introduction to Embedded Systems, Shibu K V, Secondedition, Tata McGraw Hill Education

Private Limited, 2017. (Chapters 1 and 2 selected topics)

3.LPC214x User Manual –

http://www.keil.com/dd/docs/datashts/philips/user_manual_lpc214x.pdf

http://www.keil.com/dd/docs/datashts/philips/user_manual_lpc214x.pdf

36

(LPC2148, GPIO, Registers, Embedded components selected)

Reference Books:

1. ARM System On Chip Architecture, Steve Furber, Second Edition, Pearson Education

Limited, 2000.

2. ARM ASSEMBLY LANGUAGE Fundamentals and Techniques, WilliamHohl, Christopher

Hinds, Second Edition, CRC Press, 2015.

3. ARM Assembly Language An Introduction, Gibson, Second Edition, 2007.

Course Outcomes:

• Describe the ARM processor architecture and its family.

• Develop assembly language programs to perform specific tasks using ARM instructions.

• Develop ARM microcontroller applications using Embedded C language.

• Design and develop program to interface external hardware with LPC214x

microcontroller.

ROBOTICS AND AUTOMATION



Course Objectives:

This course introduces fundamental concepts in robotics. The objective of the course is to

provide an introductory understanding of robotics. Students will be exposed to a broad range of

topics in robotics with emphasis on basics of manipulators, coordinate transformation and

kinematics, trajectory planning, control techniques, sensors and devices, robot applications and

economics analysis.

UNIT I: [08 Hrs]

BASIC CONCEPTS Automation and Robotics – An over view of Robotics – present and future

applications – classification by coordinate system and control system, Hydraulic, Pneumatic and

electric drivers – Determination HP of motor and gearing ratio.

UNIT II: [08 Hrs]

MANIPULATORS: Construction of Manipulators, Manipulator Dynamic and Force Control,

Electronic and Pneumatic manupulators.

37

ACTUATORS AND GRIPPERS Pneumatic, Hydraulic Actuators, Stepper Motor Control

Circuits, End Effecter, Various types of Grippers.

UNIT III: [07 Hrs]

TRANSFORMATION AND DYNAMICS Differential transformation and manipulators,

Jacobians – problems.Dynamics: Lagrange – Euler and Newton – Euler formations.

UNIT IV: [08 Hrs]

KINEMATICS Forward and Inverse Kinematic Problems, Solutions of Inverse Kinematic

problems,Multiple Solution, Jacobian Work Envelop – Hill Climbing Techniques.

UNIT V: [08 Hrs]

PATH PLANNING Trajectory planning and avoidance of obstacles, path planning, skew

motion, joint integrated motion – straight-line motion.

Text Books:

1. Industrial Robotics / Groover M P /Pearson Edu.

2. Fu, K.S., Gonzalez, R.C., and Lee, C.S.G., Robotics control, Sensing, Vision and

Intelligence, McGraw-Hill Publishing company, New Delhi, 2003.

3. Klafter, R.D., Chmielewski, T.A., and Negin. M, Robot Engineering-An Integrated

Approach, Prentice Hall of India, New Delhi, 2002.

4. Craig, J.J., Introduction to Robotics Mechanics and Control, Addison Wesley, 1999.

Reference Books:

1. Robotics, CSP Rao and V.V. Reddy, Pearson Publications (In press)

2. An Introduction to Robot Technology, P. Coiffet and M. Chaironze Kogam

3. Robot Analysis and Intelligence Asada and Slow time Wiley Inter-Science.

4. Robot Dynamics and Control by Mark W. Spong and M. Vidyasagar, JohnPage Ltd.

1983 London.Wiley & Sons..

Course Outcomes: 1. Understand the fundamental concepts of robot

38

2. Calculate the forward kinematics and inverse kinematics of serial and parallel robots.

3. Be able to calculate the Jacobian for serial and parallel robot.

4. Be able to do the path planning for a robotic system.

MEDICAL DEVICE DEVELOPMENT



Course Objectives:

• Understand the processes for medical device development after “design freeze”

• Become familiar with the European regulatory framework for medical devices

• Gain an understanding of manufacturing process validation

• Build on the student’s current understanding of the Quality Management System

• Understand key aspects of Product Management both during and after product launch

• Discuss Good Clinical Practices and regulations surrounding management of clinical

trials

UNIT I: [10 Hrs]

MedTech Invention: Needs finding through Observation and Problem Identification. Need

Statement Development. Need Screening & Selection through Stakeholder Analysis, Market

Analysis & Needs Filtering. Concept Generation, Screening and selection.

UNIT II: [07 Hrs]

Product Requirements: Define MedTech Device. Classification of Device. Role of

Requirements in MedTech Product Development. Market Requirements, Customer

Requirements, Clinical Workflow. Design Input. ISO 13485. Intended use, Functional /

performance requirements, safety, usability requirements etc.....

UNIT III [8 hours]

Design Engineering: Design and Development Plan. Design Process. Design Outputs,

Intermediate deliverables - System Architecture, Subsystem requirements, Prototype, System

Integration. Design Review. Design Verification.

39

UNIT IV [7 hours]

Validation: System Validation. Usability Validation. Safety Validation. Clinical Validation,

Regulatory Submission UNIT V [6 hours] Program Management: Program Planning, Stage Gate

Process, Milestones. Budgeting, Development Strategy, Risk identification and Mitigation

process.

UNIT V: [06 Hrs]

Program Management: Program Planning, Stage Gate Process, Milestones. Budgeting,

Development Strategy, Risk identification and Mitigation process.

Text Books:

1. “Biodesign: The Process of Innovating Medical Technologies”, by Stefanos Zenios , Josh

Makower, Paul Yock, Todd J. Brinton, Uday N. Kumar, Lyn Denend, Thomas M. Krummel

published by Cambridge University Press; 2nd edition.

Reference Books:

1. “Inventing medical devices: A perspective from India”, by Dr Jagdish Chaturvedi,

CreateSpace Independent Publishing Platform; 1st edition, 2015.

2. “The Medical Device R&D Handbook”, by Theodore R. Kucklick, Second Edition, CRC

Press, 2012.

Course Outcomes:

• Identify and analyse unmet clinical need and its requirements to solve it.

• Search, analyse and document clinical practice, engineering science and relevant

literature in order to determine the need for further research and development in a chosen

clinical area.

• Develop a sustainable business plan, including market overview, regulation strategies for

health & safety of individuals and intellectual property (IP) strategies.

• . Understand medical device design engineering and manufacturing process by avoiding

common quality pitfalls in turn learning project management.

VIRTUAL BMI


40


Course Objectives:

The main goal of this course is for students to learn applications of programming, signal

transduction, data acquisition, data analysis, and signal processing used in the design of medical

and laboratory instrumentation. The software package LabVIEW has become a standard in

academic and industrial environments for data acquisition, interfacing of instruments and

instrumentation control. Students will learn LabVIEW as a tool for the design of computer-based

virtual instruments, which add software-based intelligence to sensors and basic laboratory bench

devices.

UNIT I: [07 Hrs]

Graphical System Design (GSD): Introduction, GSD model, Design flow with GSD, Virtual

Instrumentation, Virtual Instrumentation and traditional instrumentation, Hardware and software

in virtual instrumentation, Virtual Instrumentation for test, control and design, GSD using

LabVIEW, Graphical programming and textural programming. Introduction to LabVIEW:

Introduction, Advantages of LabVIEW, Advantages of LabVIEW, Software environment,

Creating and saving a VI, Front panel toolbar, Block diagram toolbar, Palettes, Shortcut menus,

Property dialog boxes, Front panel controls and indicators, Block diagram, Data types, Data flow

program, LabVIEW documentation resources, Keyword shortcuts.

UNIT II: [08 Hrs]

Modular Programming: Introduction, Modular Programming in LabVIEW, Build a VI front

panel and block diagram, ICON and connector pane, Creating an icon, Building a connector

pane, Displaying subVIs and express Vis as icons or expandable nodes, Creating subVIs from

sections of a VI, Opening and editing subVIs, Placing subVIs on block diagrams, Saving subVIs,

Creating a stand-alone application. Data Acquisition: DAQ software architecture, DAQ assistant,

Channels and task configurations, Selecting and configuring a data acquisition device,

Components of computer based measurement system.

UNIT III: [08 Hrs]

General Goals of Virtual Bio-Instrumentation (VBI): Definition of VBI and importance,

General Goals of VBI applications. Basic Concepts: DAQ basics, LabVIEW basics, BioBench

basics. Neuromuscular Electrophysiology (Electromyography): Physiological basis, Experiment

set up, Experiment descriptions, Trouble shooting the nerve –Muscle Preparation. Cardiac

Electrophysiology (Electrocardiology):Physiological basis, Experiment descriptions.

Cardiopulmonary Applications: Cardiopulmonary measurement system, Hiw the

Cardiopulmonary measurement system works, Clinical Significance.

UNIT IV: [08 Hrs]

Medical Device Development Applications: The Endotester – A Virtual Instrument –Based

Quality control and Technology, Assessment System for surgicalvideoSystems: Introduction,

Materials and Methods, Endoscope Tests, Results, Discussion. Fluid Sense Innovative IV Pump

Testing: Introduction, The test System, Training Emulator.

UNIT V: [08 Hrs]

41

Healthcare Information management Systems: Medical Informatics: Defining medical

informatics, Computers in medicine, Electronic Medical record, Computerized physician order

entry, Decision support. Information Retrieval, Medical Imaging, Patient Monitoring, Medical

Education, Medical Simulation. Managing Disparate Information: ActiveX, ActiveX Data

Objects(ADO), Dynamic Link Libraries, Database Connectivity, Integrated Dashboards.

Text Books:

1. Virtual Instrumentation using LabVIEW by Jovitha Jerome, PHI Learning Private Limited,

2010. (Module 1 & 2)

2. “Virtual Bio-Instrumentation” Biomedical, Clinical, and Healthcare Applications in Lab

VIEW. ,by Jon B. Olansen and Eric Rosow, Prentice Hall Publication, 2002.

Course Outcomes:

1. Describe the Graphical System Design approach & basic features and techniques of Lab

VIEW.

2. Use the Modular Programming concepts for creation of VIs & employ DAQ assistant for

configuration of hardware devices.

3. Describe the Lab VIEW and BioBench software for EMG, ECG, and Cardiopulmonary system

analysis.

4. Explain the Medical Device Development Applications for Surgical Video Systems and

Healthcare Information Management Systems using Information Science and Technology.

Project Work-Phase-1

Code: ML7PW01 Credits: 0-0-8-4

Phase-1- Literature survey/synopsis/Seminar --4 Credits 50 M

Project Work-Phase-II

Code: ML8PW02 Credits: 0-0-8-4

Phase II - --------- 10 Credits

Project Presentation+ Project Report + Internal Viva --- 5Credits --- 100M External Viva (Demonstration +Desertation+Seminar+Vivavoce) --- 5 Credits --- 100M

Technical Seminar

Code: ML8TS01 Credits: 0-0-2-1

Seminar/Report --1 Credits 50 M

Students are required to present a technical seminar on advanced trends in biomedical field and provide

a detailed report on the same.

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